Associations of longitudinal D-Dimer and Factor II on early trauma survival risk

Background Trauma-induced coagulopathy (TIC) is a disorder that occurs in one-third of severely injured trauma patients, manifesting as increased bleeding and a 4X risk of mortality. Understanding the mechanisms driving TIC, clinical risk factors are essential to mitigating this coagulopathic bleeding and is therefore essential for saving lives. In this retrospective, single hospital study of 891 trauma patients, we investigate and quantify how two prominently described phenotypes of TIC, consumptive coagulopathy and hyperfibrinolysis, affect survival odds in the first 25 h, when deaths from TIC are most prevalent. Methods We employ a joint survival model to estimate the longitudinal trajectories of the protein Factor II (% activity) and the log of the protein fragment D-Dimer ( $$\upmu$$ μ g/ml), representative biomarkers of consumptive coagulopathy and hyperfibrinolysis respectively, and tie them together with patient outcomes. Joint models have recently gained popularity in medical studies due to the necessity to simultaneously track continuously measured biomarkers as a disease evolves, as well as to associate them with patient outcomes. In this work, we estimate and analyze our joint model using Bayesian methods to obtain uncertainties and distributions over associations and trajectories. Results We find that a unit increase in log D-Dimer increases the risk of mortality by 2.22 [1.57, 3.28] fold while a unit increase in Factor II only marginally decreases the risk of mortality by 0.94 [0.91,0.96] fold. This suggests that, while managing consumptive coagulopathy and hyperfibrinolysis both seem to affect survival odds, the effect of hyperfibrinolysis is much greater and more sensitive. Furthermore, we find that the longitudinal trajectories, controlling for many fixed covariates, trend differently for different patients. Thus, a more personalized approach is necessary when considering treatment and risk prediction under these phenotypes. Conclusion This study reinforces the finding that hyperfibrinolysis is linked with poor patient outcomes regardless of factor consumption levels. Furthermore, it quantifies the degree to which measured D-Dimer levels correlate with increased risk. The single hospital, retrospective nature can be understood to specify the results to this particular hospital’s patients and protocol in treating trauma patients. Expanding to a multi-hospital setting would result in better estimates about the underlying nature of consumptive coagulopathy and hyperfibrinolysis with survival, regardless of protocol. Individual trajectories obtained with these estimates can be used to provide personalized dynamic risk prediction when making decisions regarding management of blood factors.

[1]  T. Orfeo,et al.  The Prothrombotic Phenotypes in Familial Protein C Deficiency Are Differentiated by Computational Modeling of Thrombin Generation , 2012, PloS one.

[2]  J. Eikelboom,et al.  A Test in Context: D-Dimer. , 2017, Journal of the American College of Cardiology.

[3]  Dimitris Rizopoulos,et al.  A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time‐to‐event , 2011, Statistics in medicine.

[4]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[5]  S. Kushimoto,et al.  High D-Dimer Levels Predict a Poor Outcome in Patients with Severe Trauma, Even with High Fibrinogen Levels on Arrival: A Multicenter Retrospective Study , 2016, Shock.

[6]  G. Dobson,et al.  Mechanisms of early trauma-induced coagulopathy: The clot thickens or not? , 2015, The journal of trauma and acute care surgery.

[7]  E. Bulger,et al.  Clinical and mechanistic drivers of acute traumatic coagulopathy , 2013, The journal of trauma and acute care surgery.

[8]  M. Cohen,et al.  Coagulopathy of Trauma. , 2017, Critical care clinics.

[9]  E. Moore,et al.  Major abdominal vascular trauma--a unified approach. , 1982, The Journal of trauma.

[10]  David B. Dunson,et al.  Bayesian data analysis, third edition , 2013 .

[11]  W. Voelckel,et al.  Trauma-associated hyperfibrinolysis , 2012, Hämostaseologie.

[12]  Cécile Proust-Lima,et al.  Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach , 2014, Statistics in medicine.

[13]  C. Guerriero,et al.  The CRASH-2 trial: a randomised controlled trial and economic evaluation of the effects of tranexamic acid on death, vascular occlusive events and transfusion requirement in bleeding trauma patients. , 2013, Health technology assessment.

[14]  Joseph G Ibrahim,et al.  Basic concepts and methods for joint models of longitudinal and survival data. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  P. Zheng,et al.  Prognosis analysis and risk factors related to progressive intracranial haemorrhage in patients with acute traumatic brain injury , 2012, Brain Injury.

[16]  J. Gram,et al.  Increased levels of fibrinolysis reaction products (D-dimer) in patients with decompensated alcoholic liver cirrhosis. , 1991, Scandinavian journal of gastroenterology.

[17]  E. Bulger,et al.  The prospective, observational, multicenter, major trauma transfusion (PROMMTT) study: comparative effectiveness of a time-varying treatment with competing risks. , 2013, JAMA surgery.

[18]  A. Sauaia,et al.  Goal-directed Hemostatic Resuscitation of Trauma-induced Coagulopathy: A Pragmatic Randomized Clinical Trial Comparing a Viscoelastic Assay to Conventional Coagulation Assays , 2016, Annals of surgery.

[19]  P. Sathe,et al.  D Dimer in acute care , 2014, International journal of critical illness and injury science.

[20]  J. Vincent,et al.  Management of bleeding and coagulopathy following major trauma: an updated European guideline , 2013, Critical Care.

[21]  Adam R. Ferguson,et al.  A principal component analysis of coagulation after trauma , 2013, The journal of trauma and acute care surgery.

[22]  H. Ishikura,et al.  Trauma-induced coagulopathy and critical bleeding: the role of plasma and platelet transfusion , 2017, Journal of Intensive Care.

[23]  Anastasios A. Tsiatis,et al.  Joint Modeling of Longitudinal and Time-to-Event Data : An Overview , 2004 .

[24]  Robert Fox,et al.  Joint longitudinal and time-to-event models for multilevel hierarchical data , 2018, Statistical methods in medical research.

[25]  Angela M Wood,et al.  The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study , 2016, Statistics in medicine.

[26]  M. Maegele The coagulopathy of trauma , 2014, European Journal of Trauma and Emergency Surgery.

[27]  I. Roberts,et al.  Effect of tranexamic acid on coagulation and fibrinolysis in women with postpartum haemorrhage (WOMAN-ETAC): a single-centre, randomised, double-blind, placebo-controlled trial , 2018, Wellcome open research.

[28]  I. Roberts,et al.  What concentration of tranexamic acid is needed to inhibit fibrinolysis? A systematic review of pharmacodynamics studies , 2019, Blood coagulation & fibrinolysis : an international journal in haemostasis and thrombosis.

[29]  S. Stanworth,et al.  The incidence and magnitude of fibrinolytic activation in trauma patients , 2013, Journal of thrombosis and haemostasis : JTH.

[30]  E. Medina,et al.  The role of coagulation/fibrinolysis during Streptococcus pyogenes infection , 2014, Front. Cell. Infect. Microbiol..

[31]  E. Moore,et al.  Management of Trauma-Induced Coagulopathy with Thrombelastography. , 2017, Critical care clinics.

[32]  Jiqiang Guo,et al.  Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.

[33]  Linda R. Petzold,et al.  Identification of disease states associated with coagulopathy in trauma , 2016, BMC Medical Informatics and Decision Making.